#from google.colab import drive
#drive.mount('/content/drive')

Importing the basic necessary python libraries and modules

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
pd.options.display.max_columns = None
pd.options.display.max_rows = None

# Reading the dataset

project_path = '/content/drive/My Drive/Colab/'
file_name ='input_data.xlsx'

df = pd.read_excel(r'C:\Users\Nishant\Downloads\input_data.xlsx', encoding='utf-8')
df.head()

#df=pd.read_excel(project_path+file_name)

#Displaying the top 10 records of the dataframe

df.head(10)

#Displaying the data type of each attribute

df.dtypes

Short description    object
Description          object
Caller               object
Assignment group     object
dtype: object

#Displaying the shape of the dataframe

df.shape

(8500, 4)

This shows that there are 8500 rows and 4 attributes in the dataframe.

# Displaying the information regarding the attributes

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8500 entries, 0 to 8499
Data columns (total 4 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   Short description  8492 non-null   object
 1   Description        8499 non-null   object
 2   Caller             8500 non-null   object
 3   Assignment group   8500 non-null   object
dtypes: object(4)
memory usage: 265.8+ KB

Since Caller column mainly contains the details of the user who raised the incident and is of not much use in our analysis and can be dropped. Assignment group is our predictor / target column with multiple classes. So, this is a Multiclass Classification problem..

# Dropping the caller column

df1 = df.drop('Caller',axis=1)

# Displaying the top 5 records of the dataframe after dropping the Caller column

df1.head()

#Checking the null/missing values in the dataframe

df1.isnull().sum()

Short description    8
Description          1
Assignment group     0
dtype: int64

!pip install missingno

Requirement already satisfied: missingno in c:\users\nishant\anaconda3\lib\site-packages (0.4.2)
Requirement already satisfied: numpy in c:\users\nishant\anaconda3\lib\site-packages (from missingno) (1.18.5)
Requirement already satisfied: seaborn in c:\users\nishant\anaconda3\lib\site-packages (from missingno) (0.10.1)
Requirement already satisfied: scipy in c:\users\nishant\anaconda3\lib\site-packages (from missingno) (1.4.1)
Requirement already satisfied: matplotlib in c:\users\nishant\anaconda3\lib\site-packages (from missingno) (3.2.2)
Requirement already satisfied: pandas>=0.22.0 in c:\users\nishant\anaconda3\lib\site-packages (from seaborn->missingno) (1.0.5)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\users\nishant\anaconda3\lib\site-packages (from matplotlib->missingno) (2.4.7)
Requirement already satisfied: python-dateutil>=2.1 in c:\users\nishant\anaconda3\lib\site-packages (from matplotlib->missingno) (2.8.1)
Requirement already satisfied: kiwisolver>=1.0.1 in c:\users\nishant\anaconda3\lib\site-packages (from matplotlib->missingno) (1.2.0)
Requirement already satisfied: cycler>=0.10 in c:\users\nishant\anaconda3\lib\site-packages (from matplotlib->missingno) (0.10.0)
Requirement already satisfied: pytz>=2017.2 in c:\users\nishant\anaconda3\lib\site-packages (from pandas>=0.22.0->seaborn->missingno) (2020.1)
Requirement already satisfied: six>=1.5 in c:\users\nishant\anaconda3\lib\site-packages (from python-dateutil>=2.1->matplotlib->missingno) (1.15.0)

import missingno as msno

# Visualizing the number of missing values as a bar chart  

msno.bar(df1)

<matplotlib.axes._subplots.AxesSubplot at 0x24736d30c40>

Thus we can observe from here that their are total 8 NaN values in short description and 1 NaN value in Description attribute

#Displaying the records with NaN value in Short Description and Description column

df1[df1.isnull().any(axis=1)]

# Filling the NaN value in Short Description and Description column by space(" ")

df1['Short description'].fillna(value=" ",inplace=True)
df1['Description'].fillna(value = " ",inplace=True)

#Again Checking the null/missing values in the dataframe

df1.isnull().sum()

Short description    0
Description          0
Assignment group     0
dtype: int64

#Concatenate Short Description and Description columns in a new column "New Description"

df1['New Description'] = df1['Short description'] + ' ' +df1['Description']

#Displaying the first 5 records of the new concatenated dataframe

df1.head()

# Displaying the Duplicate recors in the dataframe

df_copy = df1[['Short description', 'Description','Assignment group','New Description']].copy()
duplicateRowsDF = df_copy[df_copy.duplicated()]
duplicateRowsDF

print("Total Number of duplicate records are:",duplicateRowsDF['Description'].count() )

Total Number of duplicate records are: 591

# Removing Duplicate records 

df_updt =df1.drop_duplicates(['Short description', 'Description', 'Assignment group','New Description'])

#Displaying the shape of the dataframe after removing the duplicate records

df_updt.shape

(7909, 4)

# Checking number of unique groups in the dataframe

df_updt['Assignment group'].unique()

array(['GRP_0', 'GRP_1', 'GRP_3', 'GRP_4', 'GRP_5', 'GRP_6', 'GRP_7',
       'GRP_8', 'GRP_9', 'GRP_10', 'GRP_11', 'GRP_12', 'GRP_13', 'GRP_14',
       'GRP_15', 'GRP_16', 'GRP_17', 'GRP_18', 'GRP_19', 'GRP_2',
       'GRP_20', 'GRP_21', 'GRP_22', 'GRP_23', 'GRP_24', 'GRP_25',
       'GRP_26', 'GRP_27', 'GRP_28', 'GRP_29', 'GRP_30', 'GRP_31',
       'GRP_33', 'GRP_34', 'GRP_35', 'GRP_36', 'GRP_37', 'GRP_38',
       'GRP_39', 'GRP_40', 'GRP_41', 'GRP_42', 'GRP_43', 'GRP_44',
       'GRP_45', 'GRP_46', 'GRP_47', 'GRP_48', 'GRP_49', 'GRP_50',
       'GRP_51', 'GRP_52', 'GRP_53', 'GRP_54', 'GRP_55', 'GRP_56',
       'GRP_57', 'GRP_58', 'GRP_59', 'GRP_60', 'GRP_61', 'GRP_32',
       'GRP_62', 'GRP_63', 'GRP_64', 'GRP_65', 'GRP_66', 'GRP_67',
       'GRP_68', 'GRP_69', 'GRP_70', 'GRP_71', 'GRP_72', 'GRP_73'],
      dtype=object)

#Displaying the basic description of Assignment group column

df_updt["Assignment group"].describe()

count      7909
unique       74
top       GRP_0
freq       3429
Name: Assignment group, dtype: object

From this we can observe that their are total 74 unique target groups in our dataframe

# Displaying the total number of tickets falling under each Assignment group with their respective percentage 

df2 = df_updt['Assignment group'].value_counts().reset_index()
df2['Percentage'] = (df2['Assignment group']/df2['Assignment group'].sum())*100
df2.head(10)

#Displaying the total number of tickets falling under each Assignment group with their respective percentage 

import seaborn as sns
import warnings

sns.set(style="whitegrid")
plt.figure(figsize=(20,5))
ax = sns.countplot(x="Assignment group", data=df_updt, order=df_updt["Assignment group"].value_counts().index)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
for p in ax.patches:
 ax.annotate(str(format(p.get_height()/len(df_updt.index)*100, '.2f')+"%"), (p.get_x() + p.get_width() / 2., p.get_height()), ha = 'center', va = 'bottom', rotation=90, xytext = (0, 10), textcoords = 'offset points')

plt.tight_layout()
plt.show()

From this we can observe that Assignment group attribute is highly right skewed . Also, more than 40% of the data i.e, around 3429 records falls under Group 1 , thus implying that the dataframe is exteremly imbalance.

# Displaying the top 20 Assignment groups with highest number of tickets falling under them in ascending order 

df_top_20 = df_updt['Assignment group'].value_counts().nlargest(20).reset_index()
df_top_20

# Visualizing the top 20 assignment groups 

plt.figure(figsize=(12,6))
bars = plt.bar(df_top_20['index'],df_top_20['Assignment group'])
plt.title('Top 20 Assignment groups with highest number of tickets falling under them')
plt.xlabel('Assignment Group')
plt.xticks(rotation=90)
plt.ylabel('Number of Tickets')

for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x(), yval + .005, yval)
plt.tight_layout()
plt.show()

# Displaying the bottom 20 Assignment groups with minimum number of tickets falling under them in descending order 

df_bottom_20 = df_updt['Assignment group'].value_counts().nsmallest(20).reset_index()
df_bottom_20

# Visualizing the bottom 20 assignment groups  

plt.figure(figsize=(12,6))
bars = plt.bar(df_bottom_20['index'],df_bottom_20['Assignment group'])
plt.title('Bottom 20 Assignment groups with minimum number of tickets falling under them')
plt.xlabel('Assignment Group')
plt.xticks(rotation=90)
plt.ylabel('Number of Tickets')
for bar in bars:
    yval = bar.get_height()
    plt.text(bar.get_x(), yval + .005, yval)
plt.tight_layout()
plt.show()

# Displaying a record before cleaning the garbled data

df_updt["New Description"][186]

'é\x9d’å²›å…´å\x90ˆæœºç”µshipment notificationé‚®ç®±è®¾ç½® from:  \nsent: friday, october 28, 2016 7:20 am\nto: nwfodmhc exurcwkm\nsubject: re: é\x9d’å²›å…´å\x90ˆæœºç”µshipment notificationé‚®ç®±è®¾ç½®\n\ndearï¼Œ\npls help to update customer 4563729890 shipment notification email address :  abcdegy@gmail.com \n\n\nb. '

# Displaying another record before cleaning the garbled data 

df_updt["New Description"][281]

'unable to down load ethics module  from: brdhdd dhwduw\nsent: thursday, october 27, 2016 6:12 am\nto: nwfodmhc exurcwkm\nsubject::fwd: unable to down load ethics module \n\n\nbegin forwarded message:\nfrom:  <dqwhpjxy.pozjxbey@gmail.com>\nto:  <zanivrec.capbfhur@gmail.com>\nsubject: unable to down load ethics module \nhi  â€“ trust doing well . i am unable to down load & getting below msg. i did reset resolution however still same issue persist.\n \nplease help.\n \n \n \n \n \ndirector of sales \ncompany indirect channels  - asia \n& \ndqwhpjxy.pozjxbey@gmail.com \n\n \n \n \n \n\n\n'

! pip install ftfy

Requirement already satisfied: ftfy in c:\users\nishant\anaconda3\lib\site-packages (5.8)
Requirement already satisfied: wcwidth in c:\users\nishant\anaconda3\lib\site-packages (from ftfy) (0.2.5)

from ftfy import *

val = df_updt.loc[: ,'New Description']

val.head()

0    login issue -verified user details.(employee# ...
1    outlook \r\n\r\nreceived from: hmjdrvpb.komuay...
2    cant log in to vpn \r\n\r\nreceived from: eylq...
3    unable to access hr_tool page unable to access...
4                            skype error  skype error 
Name: New Description, dtype: object

df_updt['New Description'] = val.apply(fix_text)

<ipython-input-34-23ad1d1011f4>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_updt['New Description'] = val.apply(fix_text)

#After using fify.fix_text on the given dataframe
df_updt["New Description"][186]

'青岛兴合机电shipment notification邮箱设置 from:  \nsent: friday, october 28, 2016 7:20 am\nto: nwfodmhc exurcwkm\nsubject: re: 青岛兴合机电shipment notification邮箱设置\n\ndear,\npls help to update customer 4563729890 shipment notification email address :  abcdegy@gmail.com \n\n\nb. '

df_updt["New Description"][281]

'unable to down load ethics module  from: brdhdd dhwduw\nsent: thursday, october 27, 2016 6:12 am\nto: nwfodmhc exurcwkm\nsubject::fwd: unable to down load ethics module \n\n\nbegin forwarded message:\nfrom:  <dqwhpjxy.pozjxbey@gmail.com>\nto:  <zanivrec.capbfhur@gmail.com>\nsubject: unable to down load ethics module \nhi  – trust doing well . i am unable to down load & getting below msg. i did reset resolution however still same issue persist.\n \nplease help.\n \n \n \n \n \ndirector of sales \ncompany indirect channels  - asia \n& \ndqwhpjxy.pozjxbey@gmail.com \n\n \n \n \n \n\n\n'

# Defining various function for cleaning the data 

import re  
#Remove numbers(integers)

def removeNumbers(text):
    text = ''.join([i for i in text if not i.isdigit()])         
    return text

#Replace Contractions

contraction_patterns = [ (r'won\'t', 'will not'),(r'didn\'t', 'did not'),(r'didnt', 'did not'), (r'can\'t', 'cannot'),(r'cant', 'cannot'), (r'i\'m', 'i am'), (r'ain\'t', 'is not'), (r'(\w+)\'ll', '\g<1> will'), (r'(\w+)n\'t', '\g<1> not'),
                         (r'(\w+)\'ve', '\g<1> have'), (r'(\w+)\'s', '\g<1> is'), (r'(\w+)\'re', '\g<1> are'), (r'(\w+)\'d', '\g<1> would'), (r'&', 'and'), (r'dammit', 'damn it'), (r'dont', 'do not'), (r'wont', 'will not') ]

def replaceContraction(text):
    patterns = [(re.compile(regex), repl) for (regex, repl) in contraction_patterns]
    for (pattern, repl) in patterns:
        (text, count) = re.subn(pattern, repl, text)
    return text


#Remove mail related words
       
def clean_data(text):
    text = re.sub(r"received from:",' ',text)
    text = re.sub(r"from:",' ',text)
    text = re.sub(r"to:",' ',text)
    text = re.sub(r"subject:",' ',text)
    text = re.sub(r"sent:",' ',text)
    text = re.sub(r"ic:",' ',text)
    text = re.sub(r"cc:",' ',text)
    text = re.sub(r"bcc:",' ',text)
    text = re.sub(r"hi",' ',text)
    text = re.sub(r"hello",' ',text)
    text = re.sub(r"com",' ',text)
    text = re.sub(r"gmail",' ',text)
    #Remove email 
    text = re.sub(r'\S*@\S*\s?', '', text)
    # Remove new line characters 
    text = re.sub(r'\n',' ',text)
    # Remove hashtag while keeping hashtag text
    text = re.sub(r'#','', text)
    #& 
    text = re.sub(r'&;?', 'and',text)
    # Remove HTML special entities (e.g. &amp;)
    text = re.sub(r'\&\w*;', '', text)
    # Remove hyperlinks
    text = re.sub(r'https?:\/\/.*\/\w*', '', text)  
    # Remove characters beyond Readable formart by Unicode:
    text= ''.join(c for c in text if c <= '\uFFFF') 
    text = text.strip()
    # Remove unreadable characters  (also extra spaces)
    text = ' '.join(re.sub("[^\u0030-\u0039\u0041-\u005a\u0061-\u007a]", " ", text).split())
    
    return text

def cleandata(text):
    # remove numbers
    text = removeNumbers(text)
    
    #remove punctuations
    text = re.sub(r"\W", " ", text, flags=re.I)
    text = text.replace('_',' ')
    
    #replace contractions
    text = replaceContraction(text)
      
    #convert to lower case
    text = text.lower()
    
    #remove mail related words
    text = clean_data(text)
            
    return text

# Applying the cleandata function to clean the dataframe

df_updt["New Description"] = val.apply(cleandata)

<ipython-input-39-fd9a973f75cf>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_updt["New Description"] = val.apply(cleandata)

#Displaying a record after the cleaning the data

df_updt["New Description"][281]

'unable to down load et cs module from brdhdd dhwduw sent thursday october am to nwfodmhc exurcwkm subject fwd unable to down load et cs module begin forwarded message from dqwhpjxy pozjxbey to zanivrec capbfhur subject unable to down load et cs module trust doing well i am unable to down load getting below msg i did reset resolution however still same issue persist please help director of sales pany indirect channels asia dqwhpjxy pozjxbey'

! pip install langdetect

Requirement already satisfied: langdetect in c:\users\nishant\anaconda3\lib\site-packages (1.0.8)
Requirement already satisfied: six in c:\users\nishant\anaconda3\lib\site-packages (from langdetect) (1.15.0)

# Defining  a function for the detection of the various languages in the dataframe

from langdetect import detect
    
def fn_lan_detect(df):                                        
   try:                                                          
      return detect(df)                                      
   except:                                                       
      return 'no'                                                  

df_updt['Language'] = val.apply(fn_lan_detect)

<ipython-input-42-659ded715740>:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_updt['Language'] = val.apply(fn_lan_detect)

# Displaying the languages present in the dataframe with their respective number of record in the dataframe

df_updt["Language"].value_counts()

en    6689
de     376
af     178
no      99
it      95
fr      81
nl      71
sv      60
es      39
ca      38
pl      33
da      30
cy      17
pt      16
tl      16
ro      15
sl       9
sq       8
hr       7
et       7
id       5
fi       5
lt       3
sk       3
lv       3
cs       2
so       2
vi       1
hu       1
Name: Language, dtype: int64

# Visualizing  the languages present in the dataframe with their respective number of record in the dataframe

x = df_updt["Language"].value_counts()
x=x.sort_index()
plt.figure(figsize=(10,6))
ax= sns.barplot(x.index, x.values, alpha=0.8)
plt.title("Distribution of text by language")
plt.ylabel('Number of records')
plt.xlabel('Language')
rects = ax.patches
labels = x.values
for rect, label in zip(rects, labels):
    height = rect.get_height()
    ax.text(rect.get_x() + rect.get_width()/2, height + 5, label, ha='center', va='bottom')

plt.show()

import nltk
nltk.download('stopwords')
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')

[nltk_data] Downloading package stopwords to
[nltk_data]     C:\Users\Nishant\AppData\Roaming\nltk_data...
[nltk_data]   Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to
[nltk_data]     C:\Users\Nishant\AppData\Roaming\nltk_data...
[nltk_data]   Package punkt is already up-to-date!
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data]     C:\Users\Nishant\AppData\Roaming\nltk_data...
[nltk_data]   Package averaged_perceptron_tagger is already up-to-
[nltk_data]       date!
[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\Nishant\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!

True

from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet, stopwords
stop = set(stopwords.words('english')) 
lemmatizer = WordNetLemmatizer()

# function to convert nltk tag to wordnet tag
def nltk_tag_to_wordnet_tag(nltk_tag):
    if nltk_tag.startswith('J'):
        return wordnet.ADJ
    elif nltk_tag.startswith('V'):
        return wordnet.VERB
    elif nltk_tag.startswith('N'):
        return wordnet.NOUN
    elif nltk_tag.startswith('R'):
        return wordnet.ADV
    else:          
        return None

def lemmatize_sentence(sentence):
    #tokenize the sentence and find the POS tag for each token
    nltk_tagged = nltk.pos_tag(nltk.word_tokenize(sentence))  
    #tuple of (token, wordnet_tag)
    wordnet_tagged = map(lambda x: (x[0], nltk_tag_to_wordnet_tag(x[1])), nltk_tagged)
    lemmatized_sentence = []
    for word, tag in wordnet_tagged:
        if tag is None:
            #if there is no available tag, append the token as is
            lemmatized_sentence.append(word)
        else:        
            #else use the tag to lemmatize the token
            lemmatized_sentence.append(lemmatizer.lemmatize(word, tag))
    return " ".join(lemmatized_sentence)

temp =[]
for sentence in df_updt["New Description"]:
    sentence = sentence.lower()
    l_sentence = lemmatize_sentence(sentence)
    words = [word for word in l_sentence.split() if word not in stopwords.words('english')]
    temp.append(words)

df_updt["Lemmatized clean"] = temp

<ipython-input-48-e811b5be8ed4>:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_updt["Lemmatized clean"] = temp

df_updt.to_excel("TempOutput_1.xlsx")

df_updt.head()

df_updt.isnull().sum()

Short description    0
Description          0
Assignment group     0
New Description      0
Language             0
Lemmatized clean     0
dtype: int64

from wordcloud import WordCloud
def wordcloud_grp(f, x):
    wordclouds_0=' '.join(map(str, f))

    wc = WordCloud(width=480, height=480, max_font_size=40, min_font_size=10, max_words=150).generate(wordclouds_0)
    plt.figure(figsize=(20,10))
    plt.imshow(wc, interpolation="bilinear")
    plt.axis("off")
    plt.title("Most common 50 words of {}".format(x))
    plt.margins(x=0, y=0)
    plt.show()

value = df_updt['Assignment group'].value_counts().sort_values(ascending=False).index
value

Index(['GRP_0', 'GRP_8', 'GRP_24', 'GRP_12', 'GRP_9', 'GRP_2', 'GRP_19',
       'GRP_3', 'GRP_6', 'GRP_13', 'GRP_10', 'GRP_5', 'GRP_14', 'GRP_25',
       'GRP_33', 'GRP_4', 'GRP_29', 'GRP_18', 'GRP_16', 'GRP_31', 'GRP_7',
       'GRP_17', 'GRP_34', 'GRP_26', 'GRP_40', 'GRP_28', 'GRP_41', 'GRP_30',
       'GRP_15', 'GRP_42', 'GRP_20', 'GRP_45', 'GRP_22', 'GRP_1', 'GRP_11',
       'GRP_21', 'GRP_47', 'GRP_23', 'GRP_48', 'GRP_62', 'GRP_39', 'GRP_27',
       'GRP_37', 'GRP_60', 'GRP_44', 'GRP_36', 'GRP_50', 'GRP_53', 'GRP_65',
       'GRP_52', 'GRP_51', 'GRP_55', 'GRP_49', 'GRP_46', 'GRP_59', 'GRP_43',
       'GRP_32', 'GRP_66', 'GRP_56', 'GRP_58', 'GRP_63', 'GRP_38', 'GRP_68',
       'GRP_54', 'GRP_72', 'GRP_69', 'GRP_57', 'GRP_71', 'GRP_61', 'GRP_73',
       'GRP_67', 'GRP_70', 'GRP_64', 'GRP_35'],
      dtype='object')

for i in range(50):

    Grp = df_updt[df_updt['Assignment group'] == value[i]]
    Grp = Grp['Lemmatized clean']
    wordcloud_grp(Grp,value[i])

max_features = 10000
maxlen = 25

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

tokenizer = Tokenizer(num_words=max_features, split=' ')
tokenizer.fit_on_texts(df_updt["New Description"].values)
X = tokenizer.texts_to_sequences(df_updt["New Description"].values)

sequences = tokenizer.texts_to_sequences(df_updt["New Description"].values)
X = pad_sequences(sequences, maxlen = maxlen)

print(len(X))

7909

tokenizer.word_index

{'to': 1,
 'the': 2,
 'in': 3,
 'job': 4,
 'from': 5,
 'is': 6,
 'no': 7,
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 'wnkpzcmv': 999,
 'affected': 1000,
 ...}

Since the data distribution is imbalanced, use techniques like under sampling, over sampling to balance the data before feeding it to the models.¶

Steps: 1) Split the data into train & test sets. 2) Perform different techniques to balance the data. 3) Train the model. 4) Test the model using the test set.

`# This is formatted as code`

df_updt.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7909 entries, 0 to 8499
Data columns (total 6 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   Short description  7909 non-null   object
 1   Description        7909 non-null   object
 2   Assignment group   7909 non-null   object
 3   New Description    7909 non-null   object
 4   Language           7909 non-null   object
 5   Lemmatized clean   7909 non-null   object
dtypes: object(6)
memory usage: 752.5+ KB

df_to_process = df_updt.copy()

df_to_process.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7909 entries, 0 to 8499
Data columns (total 6 columns):
 #   Column             Non-Null Count  Dtype 
---  ------             --------------  ----- 
 0   Short description  7909 non-null   object
 1   Description        7909 non-null   object
 2   Assignment group   7909 non-null   object
 3   New Description    7909 non-null   object
 4   Language           7909 non-null   object
 5   Lemmatized clean   7909 non-null   object
dtypes: object(6)
memory usage: 432.5+ KB

df_to_process.drop(["Short description", "Description", "Language"],axis=1,inplace=True)

df_to_process.head(5)

def wordTokenizer(dataframe):
    tokenizer = Tokenizer(num_words=numWords,filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',lower=True,split=' ', char_level=False)
    tokenizer.fit_on_texts(dataframe)
    dataframe = tokenizer.texts_to_sequences(dataframe)
    return tokenizer,dataframe

df_to_process.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 7909 entries, 0 to 8499
Data columns (total 3 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   Assignment group  7909 non-null   object
 1   New Description   7909 non-null   object
 2   Lemmatized clean  7909 non-null   object
dtypes: object(3)
memory usage: 247.2+ KB

from sklearn import preprocessing 
  
label_encoder = preprocessing.LabelEncoder() 
  
df_to_process['Assignment group ID']= label_encoder.fit_transform(df_to_process['Assignment group']) 
df_to_process['Assignment group ID'].unique()

array([ 0,  1, 23, 34, 45, 56, 67, 72, 73,  2,  3,  4,  5,  6,  7,  8,  9,
       10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 24, 25, 27, 28,
       29, 30, 31, 32, 33, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47,
       48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 26, 59, 60, 61, 62, 63, 64,
       65, 66, 68, 69, 70, 71])

maxlen = 150
numWords = 9000
tokenizer,X = wordTokenizer(df_to_process['New Description'])
y = np.asarray(df_to_process['Assignment group'])
X = pad_sequences(X, maxlen = maxlen)

df_to_process["Assignment group"].unique()

array(['GRP_0', 'GRP_1', 'GRP_3', 'GRP_4', 'GRP_5', 'GRP_6', 'GRP_7',
       'GRP_8', 'GRP_9', 'GRP_10', 'GRP_11', 'GRP_12', 'GRP_13', 'GRP_14',
       'GRP_15', 'GRP_16', 'GRP_17', 'GRP_18', 'GRP_19', 'GRP_2',
       'GRP_20', 'GRP_21', 'GRP_22', 'GRP_23', 'GRP_24', 'GRP_25',
       'GRP_26', 'GRP_27', 'GRP_28', 'GRP_29', 'GRP_30', 'GRP_31',
       'GRP_33', 'GRP_34', 'GRP_35', 'GRP_36', 'GRP_37', 'GRP_38',
       'GRP_39', 'GRP_40', 'GRP_41', 'GRP_42', 'GRP_43', 'GRP_44',
       'GRP_45', 'GRP_46', 'GRP_47', 'GRP_48', 'GRP_49', 'GRP_50',
       'GRP_51', 'GRP_52', 'GRP_53', 'GRP_54', 'GRP_55', 'GRP_56',
       'GRP_57', 'GRP_58', 'GRP_59', 'GRP_60', 'GRP_61', 'GRP_32',
       'GRP_62', 'GRP_63', 'GRP_64', 'GRP_65', 'GRP_66', 'GRP_67',
       'GRP_68', 'GRP_69', 'GRP_70', 'GRP_71', 'GRP_72', 'GRP_73'],
      dtype=object)

maxlen = 150
numWords = 9000
tokenizer,X = wordTokenizer(df_to_process['New Description'])
y = np.asarray(df_to_process['Assignment group'])
X = pad_sequences(X, maxlen = maxlen)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10)

# import the train test split package from scikit learn
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10)

from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier

rf=RandomForestClassifier()
rf.fit(X_train,y_train)
y_pred=rf.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

Accuracy: 0.5722713864306784

GRU¶

from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.layers import Dense, Input, LSTM, Embedding, Dropout, Activation, Flatten, Bidirectional, GlobalMaxPool1D,GRU,Conv1D,MaxPooling1D
from tensorflow.keras.models import Model, Sequential
import tensorflow as tf
from sklearn import metrics
from tensorflow.keras import backend as K
import matplotlib.pyplot as plt
from tensorflow.keras.utils import plot_model
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

class GruGloveModel:
  model= Model()
  X_test=[]
  y_test=[]
  embedding_matrix=[]

  def wordTokenizer(self, dataframe):
    tokenizer = Tokenizer(num_words=numWords,filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n',lower=True,split=' ', char_level=False)
    tokenizer.fit_on_texts(dataframe)
    dataframe = tokenizer.texts_to_sequences(dataframe)
    return tokenizer,dataframe
  
  def splitData(self,X,y):

    print("Number of Samples:", len(X))
    print("Number of Labels: ", len(y))
    X_train, self.X_test, y_train, self.y_test = train_test_split(X, y, test_size=0.2, random_state=10) # changed by Abraham
    X_train, X_Val, y_train, y_Val = train_test_split(X, y, test_size=0.2, random_state=10)
    print("Number of train Samples:", len(X_train))
    print("Number of val Samples:", len(X_Val))

    return X_train, self.X_test, y_train, self.y_test, X_Val, y_Val

  def tokenizeAndEmbedding(self,dataframe):
    
    tokenizer,X = self.wordTokenizer(dataframe['New Description'])
    y = np.asarray(dataframe['Assignment group ID'])
    X = pad_sequences(X, maxlen = maxlen)

    self.embedding_matrix = np.zeros((numWords+1, 100))
    for i,word in tokenizer.index_word.items():
      if i<numWords+1:
        embedding_vector = embeddings_index.get(word)
        if embedding_vector is not None:
            self.embedding_matrix[i] = embedding_vector
    return X,y

  def train(self, dataframe, batch_size, epochs):
   
    X,y = self.tokenizeAndEmbedding(dataframe)
    X_train, _, y_train, _, X_Val, y_Val = self.splitData(X,y)
    model_history = self.fitModel(X_train,y_train,X_Val,y_Val,batch_size, epochs)
    return model_history

  def fitModel(self,X_train,y_train,X_Val,y_Val,batch_size, epochs):
    
    input_layer = Input(shape=(maxlen,),dtype=tf.int64)
    embed = Embedding(numWords+1,output_dim=100,input_length=maxlen,weights=[self.embedding_matrix], trainable=True)(input_layer)  #weights=[embedding_matrix]
    gru=GRU(128)(embed)
    drop=Dropout(0.3)(gru)
    dense =Dense(100,activation='relu')(drop)
    out=Dense(len((pd.Series(y_train)).unique())+1,activation='softmax')(dense)   

    self.model = Model(input_layer,out)
    self.model.compile(loss='sparse_categorical_crossentropy',optimizer="adam",metrics=['accuracy'])

    # self.model.summary()
    # plot_model(self.model,to_file="GRU.jpg")

    checkpoint = ModelCheckpoint('model-{epoch:03d}-{val_accuracy:03f}.h5', verbose=1, monitor='val_accuracy',save_best_only=True, mode='auto') 
    reduceLoss = ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=2, min_lr=0.0001)
    model_history = self.model.fit(X_train,y_train,batch_size=batch_size, epochs=epochs, callbacks=[checkpoint,reduceLoss], validation_data=(X_Val,y_Val))
    return model_history,self.model

  def prediction(self):
      
     pred = self.model.predict(self.X_test)
     pred = [i.argmax() for i in pred]
     accuracy=metrics.accuracy_score(self.y_test, pred)
     print("Accuracy of the model :",accuracy)
     return accuracy

  def plotModelAccuracy(self, history, modelname):
    plt.plot(history.history['accuracy'])
    plt.plot(history.history['val_accuracy'])

    plt.title(modelname+' model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train','test'], loc='upper left')
    plt.show()

    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])

    plt.title(modelname+' model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train','test'], loc='upper left')
    plt.show()

  def plotModel(self):
    self.model.summary()

from gensim.models import Word2Vec

sentences = [line.split(' ') for line in df_to_process['New Description']]
word2vec = Word2Vec(sentences=sentences,min_count=1)
word2vec.wv.save_word2vec_format(r'C:\Users\Nishant\Desktop\word2vec_vector.txt')

# load the whole embedding into memory
embeddings_index = dict()
f = open(r'C:\Users\Nishant\Desktop\word2vec_vector.txt')

for line in f:
	values = line.split()
	word = values[0]
	coefs = np.asarray(values[1:], dtype='float32')
	embeddings_index[word] = coefs
f.close()
print('Loaded %s word vectors.' % len(embeddings_index))

Loaded 15390 word vectors.

# Check how the GRU Model perform with the cleansed data
# Check how the LSTM Model perform with the cleansed data
epochs=12
gruModelRawData = GruGloveModel()
gruModelRawData_history, model = gruModelRawData.train(df_to_process,100,epochs)
gruRaw_accuracy = gruModelRawData.prediction()

Number of Samples: 7909
Number of Labels:  7909
Number of train Samples: 6327
Number of val Samples: 1582
Epoch 1/15
64/64 [==============================] - ETA: 0s - loss: 2.5812 - accuracy: 0.4787
Epoch 00001: val_accuracy improved from -inf to 0.52276, saving model to model-001-0.522756.h5
64/64 [==============================] - 24s 380ms/step - loss: 2.5812 - accuracy: 0.4787 - val_loss: 2.1533 - val_accuracy: 0.5228
Epoch 2/15
64/64 [==============================] - ETA: 0s - loss: 2.0375 - accuracy: 0.5347
Epoch 00002: val_accuracy improved from 0.52276 to 0.54298, saving model to model-002-0.542984.h5
64/64 [==============================] - 21s 329ms/step - loss: 2.0375 - accuracy: 0.5347 - val_loss: 1.9676 - val_accuracy: 0.5430
Epoch 3/15
64/64 [==============================] - ETA: 0s - loss: 1.8448 - accuracy: 0.5578
Epoch 00003: val_accuracy improved from 0.54298 to 0.56637, saving model to model-003-0.566372.h5
64/64 [==============================] - 21s 325ms/step - loss: 1.8448 - accuracy: 0.5578 - val_loss: 1.8493 - val_accuracy: 0.5664
Epoch 4/15
64/64 [==============================] - ETA: 0s - loss: 1.6806 - accuracy: 0.5791
Epoch 00004: val_accuracy improved from 0.56637 to 0.57965, saving model to model-004-0.579646.h5
64/64 [==============================] - 21s 322ms/step - loss: 1.6806 - accuracy: 0.5791 - val_loss: 1.7114 - val_accuracy: 0.5796
Epoch 5/15
64/64 [==============================] - ETA: 0s - loss: 1.5331 - accuracy: 0.5933
Epoch 00005: val_accuracy did not improve from 0.57965
64/64 [==============================] - 21s 322ms/step - loss: 1.5331 - accuracy: 0.5933 - val_loss: 1.6784 - val_accuracy: 0.5740
Epoch 6/15
64/64 [==============================] - ETA: 0s - loss: 1.3553 - accuracy: 0.6238
Epoch 00006: val_accuracy improved from 0.57965 to 0.59292, saving model to model-006-0.592920.h5
64/64 [==============================] - 19s 297ms/step - loss: 1.3553 - accuracy: 0.6238 - val_loss: 1.6618 - val_accuracy: 0.5929
Epoch 7/15
64/64 [==============================] - ETA: 0s - loss: 1.2088 - accuracy: 0.6558
Epoch 00007: val_accuracy did not improve from 0.59292
64/64 [==============================] - 21s 321ms/step - loss: 1.2088 - accuracy: 0.6558 - val_loss: 1.6504 - val_accuracy: 0.5796
Epoch 8/15
64/64 [==============================] - ETA: 0s - loss: 1.0746 - accuracy: 0.6902
Epoch 00008: val_accuracy improved from 0.59292 to 0.59482, saving model to model-008-0.594817.h5
64/64 [==============================] - 20s 318ms/step - loss: 1.0746 - accuracy: 0.6902 - val_loss: 1.6766 - val_accuracy: 0.5948
Epoch 9/15
64/64 [==============================] - ETA: 0s - loss: 0.9511 - accuracy: 0.7275
Epoch 00009: val_accuracy improved from 0.59482 to 0.61125, saving model to model-009-0.611252.h5
64/64 [==============================] - 20s 308ms/step - loss: 0.9511 - accuracy: 0.7275 - val_loss: 1.8025 - val_accuracy: 0.6113
Epoch 10/15
64/64 [==============================] - ETA: 0s - loss: 0.8160 - accuracy: 0.7662
Epoch 00010: val_accuracy did not improve from 0.61125
64/64 [==============================] - 20s 317ms/step - loss: 0.8160 - accuracy: 0.7662 - val_loss: 1.7694 - val_accuracy: 0.6075
Epoch 11/15
64/64 [==============================] - ETA: 0s - loss: 0.7714 - accuracy: 0.7746
Epoch 00011: val_accuracy did not improve from 0.61125
64/64 [==============================] - 21s 330ms/step - loss: 0.7714 - accuracy: 0.7746 - val_loss: 1.8168 - val_accuracy: 0.6018
Epoch 12/15
64/64 [==============================] - ETA: 0s - loss: 0.7353 - accuracy: 0.7906
Epoch 00012: val_accuracy did not improve from 0.61125
64/64 [==============================] - 20s 319ms/step - loss: 0.7353 - accuracy: 0.7906 - val_loss: 1.8328 - val_accuracy: 0.6087
Epoch 13/15
64/64 [==============================] - ETA: 0s - loss: 0.7209 - accuracy: 0.7881
Epoch 00013: val_accuracy did not improve from 0.61125
64/64 [==============================] - 20s 313ms/step - loss: 0.7209 - accuracy: 0.7881 - val_loss: 1.8392 - val_accuracy: 0.6062
Epoch 14/15
64/64 [==============================] - ETA: 0s - loss: 0.7119 - accuracy: 0.7941
Epoch 00014: val_accuracy did not improve from 0.61125
64/64 [==============================] - 20s 318ms/step - loss: 0.7119 - accuracy: 0.7941 - val_loss: 1.8745 - val_accuracy: 0.6113
Epoch 15/15
64/64 [==============================] - ETA: 0s - loss: 0.6943 - accuracy: 0.7936
Epoch 00015: val_accuracy did not improve from 0.61125
64/64 [==============================] - 20s 312ms/step - loss: 0.6943 - accuracy: 0.7936 - val_loss: 1.8847 - val_accuracy: 0.6049
Accuracy of the model : 0.6049304677623262

gruModelRawData.plotModel()

Model: "functional_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 150)]             0         
_________________________________________________________________
embedding (Embedding)        (None, 150, 100)          900100    
_________________________________________________________________
gru (GRU)                    (None, 128)               88320     
_________________________________________________________________
dropout (Dropout)            (None, 128)               0         
_________________________________________________________________
dense (Dense)                (None, 100)               12900     
_________________________________________________________________
dense_1 (Dense)              (None, 74)                7474      
=================================================================
Total params: 1,008,794
Trainable params: 1,008,794
Non-trainable params: 0
_________________________________________________________________

gruModelRawData.plotModelAccuracy(gruModelRawData_history, 'All Data Unsampled GRU')

from sklearn.utils import resample
maxOthers = 1000

df_updt_resampled = df_to_process[0:0]
for grp in df_to_process['Assignment group'].unique():
    df_updt_GrpDF = df_to_process[df_to_process['Assignment group'] == grp]
    resampled = resample(df_updt_GrpDF, replace=True, n_samples=int(maxOthers), random_state=123)
    df_updt_resampled = df_updt_resampled.append(resampled)

descending_order = df_updt_resampled['Assignment group'].value_counts().sort_values(ascending=False).index
plt.subplots(figsize=(22,5))
#add code to rotate the labels
ax=sns.countplot(x='Assignment group', data=df_updt_resampled, color='royalblue')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha="right")
plt.tight_layout()
plt.show()

# Check how the GRU Model perform with all the data which is cleansed & resampled to 661 to make the target balance
gruModelAllDataResampled = GruGloveModel()
gruModelAllDataResampled_history, model = gruModelAllDataResampled.train(df_updt_resampled,100,epochs)
gruResampled_accuracy = gruModelAllDataResampled.prediction()

Number of Samples: 74000
Number of Labels:  74000
Number of train Samples: 59200
Number of val Samples: 14800
Epoch 1/15
592/592 [==============================] - ETA: 0s - loss: 1.8039 - accuracy: 0.5575
Epoch 00001: val_accuracy improved from -inf to 0.84912, saving model to model-001-0.849122.h5
592/592 [==============================] - 191s 323ms/step - loss: 1.8039 - accuracy: 0.5575 - val_loss: 0.5230 - val_accuracy: 0.8491
Epoch 2/15
592/592 [==============================] - ETA: 0s - loss: 0.3491 - accuracy: 0.8987
Epoch 00002: val_accuracy improved from 0.84912 to 0.92419, saving model to model-002-0.924189.h5
592/592 [==============================] - 181s 305ms/step - loss: 0.3491 - accuracy: 0.8987 - val_loss: 0.2526 - val_accuracy: 0.9242
Epoch 3/15
592/592 [==============================] - ETA: 0s - loss: 0.2012 - accuracy: 0.9365
Epoch 00003: val_accuracy improved from 0.92419 to 0.93703, saving model to model-003-0.937027.h5
592/592 [==============================] - 161s 272ms/step - loss: 0.2012 - accuracy: 0.9365 - val_loss: 0.1942 - val_accuracy: 0.9370
Epoch 4/15
592/592 [==============================] - ETA: 0s - loss: 0.1679 - accuracy: 0.9443
Epoch 00004: val_accuracy improved from 0.93703 to 0.94189, saving model to model-004-0.941892.h5
592/592 [==============================] - 169s 286ms/step - loss: 0.1679 - accuracy: 0.9443 - val_loss: 0.1721 - val_accuracy: 0.9419
Epoch 5/15
592/592 [==============================] - ETA: 0s - loss: 0.1490 - accuracy: 0.9482
Epoch 00005: val_accuracy improved from 0.94189 to 0.94723, saving model to model-005-0.947230.h5
592/592 [==============================] - 178s 300ms/step - loss: 0.1490 - accuracy: 0.9482 - val_loss: 0.1648 - val_accuracy: 0.9472
Epoch 6/15
592/592 [==============================] - ETA: 0s - loss: 0.1403 - accuracy: 0.9510
Epoch 00006: val_accuracy improved from 0.94723 to 0.94730, saving model to model-006-0.947297.h5
592/592 [==============================] - 164s 276ms/step - loss: 0.1403 - accuracy: 0.9510 - val_loss: 0.1584 - val_accuracy: 0.9473
Epoch 7/15
592/592 [==============================] - ETA: 0s - loss: 0.1329 - accuracy: 0.9526
Epoch 00007: val_accuracy did not improve from 0.94730
592/592 [==============================] - 163s 275ms/step - loss: 0.1329 - accuracy: 0.9526 - val_loss: 0.1640 - val_accuracy: 0.9448
Epoch 8/15
592/592 [==============================] - ETA: 0s - loss: 0.1311 - accuracy: 0.9539
Epoch 00008: val_accuracy did not improve from 0.94730
592/592 [==============================] - 165s 279ms/step - loss: 0.1311 - accuracy: 0.9539 - val_loss: 0.1566 - val_accuracy: 0.9470
Epoch 9/15
592/592 [==============================] - ETA: 0s - loss: 0.1291 - accuracy: 0.9533
Epoch 00009: val_accuracy did not improve from 0.94730
592/592 [==============================] - 178s 301ms/step - loss: 0.1291 - accuracy: 0.9533 - val_loss: 0.1567 - val_accuracy: 0.9468
Epoch 10/15
592/592 [==============================] - ETA: 0s - loss: 0.1239 - accuracy: 0.9551
Epoch 00010: val_accuracy did not improve from 0.94730
592/592 [==============================] - 177s 298ms/step - loss: 0.1239 - accuracy: 0.9551 - val_loss: 0.1600 - val_accuracy: 0.9468
Epoch 11/15
592/592 [==============================] - ETA: 0s - loss: 0.1143 - accuracy: 0.9577
Epoch 00011: val_accuracy improved from 0.94730 to 0.95020, saving model to model-011-0.950203.h5
592/592 [==============================] - 160s 271ms/step - loss: 0.1143 - accuracy: 0.9577 - val_loss: 0.1534 - val_accuracy: 0.9502
Epoch 12/15
592/592 [==============================] - ETA: 0s - loss: 0.1127 - accuracy: 0.9581
Epoch 00012: val_accuracy did not improve from 0.95020
592/592 [==============================] - 144s 243ms/step - loss: 0.1127 - accuracy: 0.9581 - val_loss: 0.1530 - val_accuracy: 0.9489
Epoch 13/15
592/592 [==============================] - ETA: 0s - loss: 0.1121 - accuracy: 0.9581
Epoch 00013: val_accuracy did not improve from 0.95020
592/592 [==============================] - 153s 259ms/step - loss: 0.1121 - accuracy: 0.9581 - val_loss: 0.1526 - val_accuracy: 0.9495
Epoch 14/15
592/592 [==============================] - ETA: 0s - loss: 0.1126 - accuracy: 0.9577
Epoch 00014: val_accuracy improved from 0.95020 to 0.95088, saving model to model-014-0.950878.h5
592/592 [==============================] - 163s 276ms/step - loss: 0.1126 - accuracy: 0.9577 - val_loss: 0.1548 - val_accuracy: 0.9509
Epoch 15/15
592/592 [==============================] - ETA: 0s - loss: 0.1119 - accuracy: 0.9579
Epoch 00015: val_accuracy did not improve from 0.95088
592/592 [==============================] - 182s 307ms/step - loss: 0.1119 - accuracy: 0.9579 - val_loss: 0.1554 - val_accuracy: 0.9493
Accuracy of the model : 0.9492567567567568

gruModelAllDataResampled.plotModelAccuracy(gruModelAllDataResampled_history, 'All Data Resampled GRU')

	Short description	Description	Caller	Assignment group
0	login issue	-verified user details.(employee# & manager na...	spxjnwir pjlcoqds	GRP_0
1	outlook	\r\n\r\nreceived from: hmjdrvpb.komuaywn@gmail...	hmjdrvpb komuaywn	GRP_0
2	cant log in to vpn	\r\n\r\nreceived from: eylqgodm.ybqkwiam@gmail...	eylqgodm ybqkwiam	GRP_0
3	unable to access hr_tool page	unable to access hr_tool page	xbkucsvz gcpydteq	GRP_0
4	skype error	skype error	owlgqjme qhcozdfx	GRP_0
5	unable to log in to engineering tool and skype	unable to log in to engineering tool and skype	eflahbxn ltdgrvkz	GRP_0
6	event: critical:HostName_221.company.com the v...	event: critical:HostName_221.company.com the v...	jyoqwxhz clhxsoqy	GRP_1
7	ticket_no1550391- employment status - new non-...	ticket_no1550391- employment status - new non-...	eqzibjhw ymebpoih	GRP_0
8	unable to disable add ins on outlook	unable to disable add ins on outlook	mdbegvct dbvichlg	GRP_0
9	ticket update on inplant_874773	ticket update on inplant_874773	fumkcsji sarmtlhy	GRP_0

	Short description	Description	Assignment group
2604	NaN	\r\n\r\nreceived from: ohdrnswl.rezuibdt@gmail...	GRP_34
3383	NaN	\r\n-connected to the user system using teamvi...	GRP_0
3906	NaN	-user unable tologin to vpn.\r\n-connected to...	GRP_0
3910	NaN	-user unable tologin to vpn.\r\n-connected to...	GRP_0
3915	NaN	-user unable tologin to vpn.\r\n-connected to...	GRP_0
3921	NaN	-user unable tologin to vpn.\r\n-connected to...	GRP_0
3924	NaN	name:wvqgbdhm fwchqjor\nlanguage:\nbrowser:mic...	GRP_0
4341	NaN	\r\n\r\nreceived from: eqmuniov.ehxkcbgj@gmail...	GRP_0
4395	i am locked out of skype	NaN	GRP_0

	index	Assignment group	Percentage
0	GRP_0	3429	43.355671
1	GRP_8	645	8.155266
2	GRP_24	285	3.603490
3	GRP_12	256	3.236819
4	GRP_9	252	3.186244
5	GRP_2	241	3.047161
6	GRP_19	214	2.705778
7	GRP_3	200	2.528765
8	GRP_6	183	2.313820
9	GRP_13	145	1.833354

	index	Assignment group
0	GRP_64	1
1	GRP_70	1
2	GRP_61	1
3	GRP_73	1
4	GRP_67	1
5	GRP_35	1
6	GRP_54	2
7	GRP_72	2
8	GRP_69	2
9	GRP_57	2
10	GRP_71	2
11	GRP_56	3
12	GRP_58	3
13	GRP_63	3
14	GRP_38	3
15	GRP_68	3
16	GRP_32	4
17	GRP_66	4
18	GRP_43	5
19	GRP_59	6

	Assignment group	New Description	Lemmatized clean
0	GRP_0	login issue verified user details employee man...	[login, issue, verify, user, detail, employee,...
1	GRP_0	outlook received from hmjdrvpb komuaywn team m...	[outlook, receive, hmjdrvpb, komuaywn, team, m...
2	GRP_0	cannot log in to vpn received from eylqgodm yb...	[log, vpn, receive, eylqgodm, ybqkwiam, log, v...
3	GRP_0	unable to access hr tool page unable to access...	[unable, access, hr, tool, page, unable, acces...
4	GRP_0	skype error skype error	[skype, error, skype, error]

	Short description	Description	Assignment group	New Description
51	call for ecwtrjnq jpecxuty	call for ecwtrjnq jpecxuty	GRP_0	call for ecwtrjnq jpecxuty call for ecwtrjnq j...
81	erp SID_34 account locked	erp SID_34 account locked	GRP_0	erp SID_34 account locked erp SID_34 account l...
123	unable to display expense report	unable to display expense report	GRP_0	unable to display expense report unable to dis...
157	ess password reset	ess password reset	GRP_0	ess password reset ess password reset
229	call for ecwtrjnq jpecxuty	call for ecwtrjnq jpecxuty	GRP_0	call for ecwtrjnq jpecxuty call for ecwtrjnq j...
235	erp SID_34 account unlock and password reset	erp SID_34 account unlock and password reset	GRP_0	erp SID_34 account unlock and password reset e...
242	windows password reset	windows password reset	GRP_0	windows password reset windows password reset
274	windows account locked	windows account locked	GRP_0	windows account locked windows account locked
301	windows password reset	windows password reset	GRP_0	windows password reset windows password reset
312	erp SID_34 account unlock	erp SID_34 account unlock	GRP_0	erp SID_34 account unlock erp SID_34 account u...
333	windows password reset	windows password reset	GRP_0	windows password reset windows password reset
380	unable to login to erp SID_34	unable to login to erp SID_34	GRP_0	unable to login to erp SID_34 unable to login ...
391	password reset request	password reset request	GRP_0	password reset request password reset request
393	password reset	password reset	GRP_0	password reset password reset
422	password reset	password reset	GRP_0	password reset password reset
493	ticket update on inplant_872730	ticket update on inplant_872730	GRP_0	ticket update on inplant_872730 ticket update ...
512	blank call //gso	blank call //gso	GRP_0	blank call //gso blank call //gso
516	outlook freezing issue	outlook freezing issue	GRP_0	outlook freezing issue outlook freezing issue
526	password reset	password reset	GRP_0	password reset password reset
544	unable to open outlook	unable to open outlook	GRP_0	unable to open outlook unable to open outlook
571	erp SID_34 password reset.	erp SID_34 password reset.	GRP_0	erp SID_34 password reset. erp SID_34 password...
580	erp SID_34 account locked	erp SID_34 account locked	GRP_0	erp SID_34 account locked erp SID_34 account l...
584	erp SID_34 account unlock and password reset	erp SID_34 account unlock and password reset	GRP_0	erp SID_34 account unlock and password reset e...
587	login issue	login issue\r\n-verified user details.(employe...	GRP_0	login issue login issue\r\n-verified user deta...
662	erp SID_34 account locked	erp SID_34 account locked	GRP_0	erp SID_34 account locked erp SID_34 account l...
667	job bkbackup_tool_powder_prod_full failed in j...	received from: monitoring_tool@company.com\r\n...	GRP_8	job bkbackup_tool_powder_prod_full failed in j...
681	password reset	password reset	GRP_0	password reset password reset
713	skype error while logging in	skype error while logging in	GRP_0	skype error while logging in skype error whil...
720	blank call	blank call	GRP_0	blank call blank call
724	blank call	blank call	GRP_0	blank call blank call
741	account locked in erp SID_34	account locked in erp SID_34	GRP_0	account locked in erp SID_34 account locked in...
770	blank call	blank call	GRP_0	blank call blank call
850	password reset	password reset	GRP_0	password reset password reset
873	erp SID_34 account unlock and password reset	erp SID_34 account unlock and password reset	GRP_0	erp SID_34 account unlock and password reset e...
882	erp SID_34 account unlock and password reset	erp SID_34 account unlock and password reset	GRP_0	erp SID_34 account unlock and password reset e...
892	login issue	login issue\r\n-verified user details.(employe...	GRP_0	login issue login issue\r\n-verified user deta...
899	HostName_1030 is currently experiencing high c...	HostName_1030 is currently experiencing high c...	GRP_12	HostName_1030 is currently experiencing high c...
954	unable to access collaboration_platform	unable to access collaboration_platform	GRP_0	unable to access collaboration_platform unable...
973	unable to open outlook	unable to open outlook	GRP_0	unable to open outlook unable to open outlook
996	account unlock	account unlock	GRP_0	account unlock account unlock